46,700 research outputs found
Hemodynamic evaluation using four-dimensional flow magnetic resonance imaging for a patient with multichanneled aortic dissection
The hemodynamic function of multichanneled aortic dissection (MCAD) requires close monitoring and effective management to avoid potentially catastrophic sequelae. This report describes a 47-year-old man who underwent endovascular repair based on findings from four-dimensional (4D) flow magnetic resonance imaging of an MCAD. The acquired 4D flow data revealed complex, bidirectional flow patterns in the false lumens and accelerated blood flow in the compressed true lumen. The collapsed abdominal true lumen expanded unsatisfactorily after primary tear repair, which required further remodeling with bare stents. This case study demonstrates that hemodynamic analysis using 4D flow magnetic resonance imaging can help understand the complex pathologic changes of MCAD
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Life cycle assessment of white roof and sedum-tray garden roof for office buildings in China
White roof (WR) and Sedum lineare tray garden roof (STGR) have been convinced to improve the energy-efficiency and provide various benefits for conventional impervious grey roofs. Some national and local standards have standardized and recommended these technologies in existing building retrofits, however, they do not include assessment and choice of a particular roof retrofit in different climates. This paper presents a 40-year life-cycle cost analysis (LCCA) of an office building roof retrofitted by adding either WR or STGR over an existing grey roof in five cities, located in four Chinese climate zones. The LCCA find that the WR retrofits exhibit positive life-cycle net savings (NS) in warm winter zones, ranging 5.7–35.1 CNY/m 2 , and STGR retrofits have negative NS of -81.3– -16.7 CNY/m 2 in all climate zones. The NS of both WR and STGR generally tend to improve as one moves from the coldest cities to the warmest cities. LCCA results suggest that adding new building codes concerning crediting or prescribing WR and STGR retrofits into office buildings with grey roofs in hot summer climate zones and warm winter zone in China, respectively. And featured by more specific requirements, the localized Technical Norms help promote the implementation of new building codes
Neutrino Masses, Lepton Flavor Mixing and Leptogenesis in the Minimal Seesaw Model
We present a review of neutrino phenomenology in the minimal seesaw model
(MSM), an economical and intriguing extension of the Standard Model with only
two heavy right-handed Majorana neutrinos. Given current neutrino oscillation
data, the MSM can predict the neutrino mass spectrum and constrain the
effective masses of the tritium beta decay and the neutrinoless double-beta
decay. We outline five distinct schemes to parameterize the neutrino
Yukawa-coupling matrix of the MSM. The lepton flavor mixing and baryogenesis
via leptogenesis are investigated in some detail by taking account of possible
texture zeros of the Dirac neutrino mass matrix. We derive an upper bound on
the CP-violating asymmetry in the decay of the lighter right-handed Majorana
neutrino. The effects of the renormalization-group evolution on the neutrino
mixing parameters are analyzed, and the correlation between the CP-violating
phenomena at low and high energies is highlighted. We show that the observed
matter-antimatter asymmetry of the Universe can naturally be interpreted
through the resonant leptogenesis mechanism at the TeV scale. The
lepton-flavor-violating rare decays, such as , are also
discussed in the supersymmetric extension of the MSM.Comment: 50 pages, 22 EPS figures, macro file ws-ijmpe.cls included, accepted
for publication in Int. J. Mod. Phys.
Learning Bayesian Networks using the Constrained Maximum a Posteriori Probability Method
Purely data-driven methods often fail to learn accurate conditional probability table (CPT) parameters of discrete Bayesian networks (BNs) when training data are scarce or incomplete. A practical and efficient means of overcoming this problem is to introduce qualitative parameter constraints derived from expert judgments. To exploit such knowledge, in this paper, we provide a constrained maximum a posteriori (CMAP) method to learn CPT parameters by incorporating convex constraints. To further improve the CMAP method, we present a type of constrained Bayesian Dirichlet priors that is compatible with the given constraints. Combined with the CMAP method, we propose an improved expectation maximum algorithm to process incomplete data. Experiments are conducted on learning standard BNs from complete and incomplete data. The results show that the proposed method outperforms existing methods, especially when data are extremely limited or incomplete. This finding suggests the potential effective application of CMAP to real-world problems. Moreover, a real facial action unit (AU) recognition case with incomplete data is conducted by applying different parameter learning methods. The results show that the recognition accuracy of respective recognition methods can be improved by the AU BN, which is trained by the proposed method
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